





'''simple docstring'''






import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					lowerCAmelCase									=   False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor
class 		lowerCamelCase	(   unittest.TestCase							):



	def __init__( self		,   a_		,   a_=7		,   a_=3		,   a_=18		,   a_=30		,   a_=400		,   a_=None		,   a_=True		,   a_=True		,   a_=None		,   ):
			lowerCAmelCase  :     Union[str, Any]       						= size if size is not None else {"height": 20, "width": 20}
			lowerCAmelCase  :     List[Any]       						= parent
			lowerCAmelCase  :     Dict       						= batch_size
			lowerCAmelCase  :     Optional[Any]       						= num_channels
			lowerCAmelCase  :     Dict       						= image_size
			lowerCAmelCase  :     List[str]       						= min_resolution
			lowerCAmelCase  :     int       						= max_resolution
			lowerCAmelCase  :     Dict       						= size
			lowerCAmelCase  :     Optional[int]       						= do_normalize
			lowerCAmelCase  :     int       						= do_convert_rgb
			lowerCAmelCase  :     Any       						= [512, 1_024, 2_048, 4_096]
			lowerCAmelCase  :     List[str]       						= patch_size if patch_size is not None else {"height": 16, "width": 16}



	def 	_lowerCamelCase     ( self						):
			return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
			lowerCAmelCase  :     Any       						= Image.open(requests.get(a_		,   stream=a_						).raw						).convert("RGB"						)
			return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   , reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."   , )
@require_torch
@require_vision
class 		lowerCamelCase	(   _A   , unittest.TestCase							):
	snake_case_   = PixaStructImageProcessor if is_vision_available() else None



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= PixaStructImageProcessingTester(self						)



	@property
	def 	_lowerCamelCase     ( self						):
			return self.image_processor_tester.prepare_image_processor_dict()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     str       						= self.image_processing_class(**self.image_processor_dict						)
			self.assertTrue(hasattr(a_		,   "do_normalize"						)						)
			self.assertTrue(hasattr(a_		,   "do_convert_rgb"						)						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[int]       						= self.image_processor_tester.prepare_dummy_image()

			lowerCAmelCase  :     Any       						= self.image_processing_class(**self.image_processor_dict						)
			lowerCAmelCase  :     List[str]       						= 2_048

			lowerCAmelCase  :     Any       						= image_processor(a_		,   return_tensors="pt"		,   max_patches=a_						)
			self.assertTrue(torch.allclose(inputs.flattened_patches.mean()		,   torch.tensor(0.0606						)		,   atol=1e-3		,   rtol=1e-3						)						)



	def 	_lowerCamelCase     ( self						):
			# Initialize image_processor
			lowerCAmelCase  :     Union[str, Any]       						= self.image_processing_class(**self.image_processor_dict						)
			# create random PIL images
			lowerCAmelCase  :     Any       						= prepare_image_inputs(self.image_processor_tester		,   equal_resolution=a_						)
			for image in image_inputs:
					self.assertIsInstance(a_		,   Image.Image						)

			# Test not batched input
			lowerCAmelCase  :     str       						= (
			    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
			    * self.image_processor_tester.num_channels
			) + 2

			for max_patch in self.image_processor_tester.max_patches:
					# Test not batched input
					lowerCAmelCase  :     List[Any]       						= image_processor(
					    image_inputs[0]		,   return_tensors="pt"		,   max_patches=a_						).flattened_patches
					self.assertEqual(
					    encoded_images.shape		,   (1, max_patch, expected_hidden_dim)		,   )

					# Test batched
					lowerCAmelCase  :     Any       						= image_processor(
					    a_		,   return_tensors="pt"		,   max_patches=a_						).flattened_patches
					self.assertEqual(
					    encoded_images.shape		,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,   )



	def 	_lowerCamelCase     ( self						):
			# Initialize image_processor
			lowerCAmelCase  :     Optional[int]       						= self.image_processing_class(**self.image_processor_dict						)
			# create random PIL images
			lowerCAmelCase  :     List[Any]       						= prepare_image_inputs(self.image_processor_tester		,   equal_resolution=a_						)
			for image in image_inputs:
					self.assertIsInstance(a_		,   Image.Image						)

			# Test not batched input
			lowerCAmelCase  :     int       						= (
			    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
			    * self.image_processor_tester.num_channels
			) + 2

			lowerCAmelCase  :     Optional[int]       						= True

			for max_patch in self.image_processor_tester.max_patches:
					# Test not batched input
					with self.assertRaises(a_						):
							lowerCAmelCase  :     Dict       						= image_processor(
							    image_inputs[0]		,   return_tensors="pt"		,   max_patches=a_						).flattened_patches

					lowerCAmelCase  :     int       						= "Hello"

					lowerCAmelCase  :     Union[str, Any]       						= image_processor(
					    image_inputs[0]		,   return_tensors="pt"		,   max_patches=a_		,   header_text=a_						).flattened_patches
					self.assertEqual(
					    encoded_images.shape		,   (1, max_patch, expected_hidden_dim)		,   )

					# Test batched
					lowerCAmelCase  :     Tuple       						= image_processor(
					    a_		,   return_tensors="pt"		,   max_patches=a_		,   header_text=a_						).flattened_patches
					self.assertEqual(
					    encoded_images.shape		,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,   )



	def 	_lowerCamelCase     ( self						):
			# Initialize image_processor
			lowerCAmelCase  :     Union[str, Any]       						= self.image_processing_class(**self.image_processor_dict						)
			# create random numpy tensors
			lowerCAmelCase  :     Optional[Any]       						= prepare_image_inputs(self.image_processor_tester		,   equal_resolution=a_		,   numpify=a_						)
			for image in image_inputs:
					self.assertIsInstance(a_		,   np.ndarray						)

			lowerCAmelCase  :     int       						= (
			    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
			    * self.image_processor_tester.num_channels
			) + 2

			for max_patch in self.image_processor_tester.max_patches:
					# Test not batched input
					lowerCAmelCase  :     str       						= image_processor(
					    image_inputs[0]		,   return_tensors="pt"		,   max_patches=a_						).flattened_patches
					self.assertEqual(
					    encoded_images.shape		,   (1, max_patch, expected_hidden_dim)		,   )

					# Test batched
					lowerCAmelCase  :     Optional[int]       						= image_processor(
					    a_		,   return_tensors="pt"		,   max_patches=a_						).flattened_patches
					self.assertEqual(
					    encoded_images.shape		,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,   )





	def 	_lowerCamelCase     ( self						):
			# Initialize image_processor
			lowerCAmelCase  :     List[str]       						= self.image_processing_class(**self.image_processor_dict						)
			# create random PyTorch tensors
			lowerCAmelCase  :     Dict       						= prepare_image_inputs(self.image_processor_tester		,   equal_resolution=a_		,   torchify=a_						)
			for image in image_inputs:
					self.assertIsInstance(a_		,   torch.Tensor						)

			# Test not batched input
			lowerCAmelCase  :     int       						= (
			    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
			    * self.image_processor_tester.num_channels
			) + 2

			for max_patch in self.image_processor_tester.max_patches:
					# Test not batched input
					lowerCAmelCase  :     Optional[Any]       						= image_processor(
					    image_inputs[0]		,   return_tensors="pt"		,   max_patches=a_						).flattened_patches
					self.assertEqual(
					    encoded_images.shape		,   (1, max_patch, expected_hidden_dim)		,   )

					# Test batched
					lowerCAmelCase  :     Dict       						= image_processor(
					    a_		,   return_tensors="pt"		,   max_patches=a_						).flattened_patches
					self.assertEqual(
					    encoded_images.shape		,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,   )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   , reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."   , )
@require_torch
@require_vision
class 		lowerCamelCase	(   _A   , unittest.TestCase							):
	snake_case_   = PixaStructImageProcessor if is_vision_available() else None



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Dict       						= PixaStructImageProcessingTester(self		,   num_channels=4						)
			lowerCAmelCase  :     Dict       						= 3



	@property
	def 	_lowerCamelCase     ( self						):
			return self.image_processor_tester.prepare_image_processor_dict()



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= self.image_processing_class(**self.image_processor_dict						)
			self.assertTrue(hasattr(a_		,   "do_normalize"						)						)
			self.assertTrue(hasattr(a_		,   "do_convert_rgb"						)						)





	def 	_lowerCamelCase     ( self						):
			# Initialize image_processor
			lowerCAmelCase  :     str       						= self.image_processing_class(**self.image_processor_dict						)
			# create random PIL images
			lowerCAmelCase  :     Union[str, Any]       						= prepare_image_inputs(self.image_processor_tester		,   equal_resolution=a_						)
			for image in image_inputs:
					self.assertIsInstance(a_		,   Image.Image						)

			# Test not batched input
			lowerCAmelCase  :     Union[str, Any]       						= (
			    (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
			    * (self.image_processor_tester.num_channels - 1)
			) + 2

			for max_patch in self.image_processor_tester.max_patches:
					# Test not batched input
					lowerCAmelCase  :     Optional[int]       						= image_processor(
					    image_inputs[0]		,   return_tensors="pt"		,   max_patches=a_						).flattened_patches
					self.assertEqual(
					    encoded_images.shape		,   (1, max_patch, expected_hidden_dim)		,   )

					# Test batched
					lowerCAmelCase  :     Union[str, Any]       						= image_processor(
					    a_		,   return_tensors="pt"		,   max_patches=a_						).flattened_patches
					self.assertEqual(
					    encoded_images.shape		,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,   )










'''simple docstring'''






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for data in source_data:
				for i, el in enumerate(a_						):
						if len(a_						) < i + 1:
								data_lists.append([]						)
						data_lists[i].append(float(a_						)						)
		return data_lists






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for dlist, weight in zip(a_   ,a_						):
				lowerCAmelCase  :     Optional[Any]       						= min(a_						)
				lowerCAmelCase  :     List[str]       						= max(a_						)

				lowerCAmelCase  :     list[float]       						= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))						)
								except ZeroDivisionError:
										score.append(1						)

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)						)
								except ZeroDivisionError:
										score.append(0						)

        # weight not 0 or 1
				else:
						lowerCAmelCase  :     List[Any]       						= f'''Invalid weight of {weight:f} provided'''
						raise ValueError(a_						)

				score_lists.append(a_						)

		return score_lists






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[float]       						= [0 for i in range(len(score_lists[0]						)						)]

		for slist in score_lists:
				for j, ele in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= final_scores[j] + ele

		return final_scores






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     Union[str, Any]       						= get_data(a_						)
		lowerCAmelCase  :     List[Any]       						= calculate_each_score(a_   ,a_						)
		lowerCAmelCase  :     Optional[Any]       						= generate_final_scores(a_						)

		# append scores to source data
		for i, ele in enumerate(a_						):
				source_data[i].append(a_						)

		return source_data



